Hypertensive disorders of pregnancy after multifetal pregnancy reduction: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To systematically review the literature on hypertensive disorders of pregnancy (HDP) after multifetal pregnancy reduction (MFPR).Methods A comprehensive search in PubMed, Embase, Web of Science, and Scopus was performed. Prospective or retrospective studies reporting on MFPR from triplet or higher-order to twin compared to ongoing (i.e., non-reduced) triplets and/or twins were included. A meta-analysis of the primary outcome HDP was carried out using a random-effects model. Subgroup analyses of gestational hypertension (GH) and preeclampsia (PE) were performed. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale.Results Thirty studies with a total of 9,811 women were included. MFPR from triplet to twin was associated with a lower risk for HDP compared to ongoing triplets (OR 0.55, 95% CI, 0.37–0.83; p = 0.004). In a subgroup analysis, the decreased risk of HDP was driven by GH, and PE was no longer significant (OR 0.34, 95% CI, 0.17–0.70; p = 0.004 and OR 0.64, 95% CI, 0.38–1.09; p = 0.10, respectively). HDP was also significantly lower after MFPR from all higher-order (including triplets) to twin compared to ongoing triplets (OR 0.55, 95% CI, 0.38–0.79; p = 0.001). In a subgroup analysis, the decreased risk of HDP was driven by PE, and GH was no longer significant (OR 0.55, 95% CI 0.32–0.92; p = 0.02 and OR 0.55, 95% CI 0.28–1.06; p = 0.08, respectively). No significant differences in HDP were found in MFPR from triplet or higher-order to twin versus ongoing twins.Conclusions MFPR in women with triplet and higher-order multifetal pregnancies decreases the risk of HDP. Twelve women should undergo MFPR to prevent one event of HDP. These data can be used in the decision-making process of MFPR, in which the individual risk factors of HDP can be taken into account.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".